Robust biometric feature extraction with and without reference point

ABSTRACT

A basic idea of the present invention is to selectively employ one of at least two different feature extraction processes when generating a biometric template of an individual. An individual offers a physiological property, such as a fingerprint, an iris, an ear, a face, etc., from which biometric data can be derived, to a sensor of an enrolment authority. In the following, the property to be discussed will be fingerprints, even though any suitable biometric property may be used. From the fingerprint, a positional reference point of the biometric data is derived. The derivation of the positional reference point may be accomplished using any appropriate method out of a number of known methods. Such a reference point could be the location of a core, a delta, a weighted average of minutiae coordinates, or alike. Typically, the reference point includes a core of a fingerprint expressed as a three-dimensional coordinate denoted by means of x r , y r , and angle α r . A contribution indicator is calculated for the derived positional reference point, and it is determined whether the derived positional reference point can be considered reliable. Depending on the reliability of the derived reference point, one of the two different feature extraction processes is selected; either the first feature set is extracted using a method which is invariant of the derived reference point, or a method is used taking into account the derived reference point. The better the estimation of the reference point is, the more reliable the reference point-dependent extraction method is. Finally, the biometric template is generated using the extracted first feature set.

FIELD OF THE INVENTION

The present invention relates to a method and system of generating atemplate from biometric data associated with an individual, and a methodand system of verifying identity of an individual by employing biometricdata.

BACKGROUND OF THE INVENTION

The process of authenticating a physical object is commonly undertakenin many applications, such as conditional access to secure buildings orconditional access to digital data (e.g. stored in a computer orremovable storage media), or for identification purposes (e.g. forcharging an identified individual for a particular activity, or forboarding passengers at airports).

The use of biometrics for identification and/or authentication is to anever increasing extent considered to be a better alternative totraditional identification means such as passwords, pin-codes andauthentication tokens. In biometric identification, features that areunique to a user such as fingerprints, irises, shape of ears, facialappearance, etc. are used to provide identification of the user. Today,fingerprints are the most common biometric modality; roughly 70% of thebiometric market uses fingerprints for identity verification. Themajority of fingerprint algorithms are based on minutiae locations whichare processed in an adequate manner to form a biometric template of anindividual. These locations are estimated during enrolment andverification. During enrolment—i.e. the initial process when anenrolment authority acquires the biometric template of a user—the useroffers her biometric to an enrolment device of the enrolment authoritywhich generates and stores the template, possibly encrypted, in thesystem. During verification, the user again offers her biometric to thesystem, whereby the stored template is retrieved (and decrypted ifrequired) and matching of the stored and a newly generated template iseffected, i.e. the minutiae locations that were obtained duringenrolment are compared to those acquired during verification. If thereis a good enough match, the user is considered authenticated.

Alternative approaches use shape-related parameters, such as directionalfield of ridges and valleys of a fingerprint image. Such directionalfield is estimated as a function of the position in the fingerprint andsubsequently used as feature data (or a derivative thereof).Translations and rotations of the measurement data cause major problemswhen minutiae locations or shape-related parameters are to be matched. Auser may place her finger differently during verification than duringenrolment. In most cases, the comparison stage during verificationrequires a template alignment step before the actual comparison processis employed, in order to compensate for translation and rotationdifferences. More advanced comparison algorithms also take non-lineardistortions into account.

In order to safeguard the integrity of individuals employing a biometricidentification system whenever a breach of secrecy occurs in the system,cryptographic techniques to encrypt or hash the biometric templates andperform the verification (or matching) on the encrypted data such thatthe real template is never available in the clear can be envisaged. Withthe advent of these template protection techniques, which employencryption or one-way functions to biometric data, template alignmentduring comparison is virtually impossible. Comparison is employed in theencrypted domain, and hence there is no access to the original biometricdata for alignment or analysis purposes. As a result, alignment issueshave to be resolved as a pre-processing step before generating thetemplate.

A known method for alignment as a pre-processing step is to extractfeatures, and to correct minutiae data by means of a certain referencepoint. This reference point could be found and/or generated with thehelp of core location(s), delta location, the average minutiae location,or any other relatively stable, reproducible reference location withinthe fingerprint image. If features are defined relative to thisreference point, and this process is defined similarly for enrolment andverification, there is no need for an additional alignment step duringcomparison in the verification phase. Although the method of employing areference point is quite successful in many cases, it can also causeproblems. From empirical tests, it is estimated that forstate-of-the-art fingerprint analysis algorithms, about 10% of thefingerprints do not have a reliable reference point (e.g., a unique corelocation). In such cases, alignment is performed using a badly estimatedreference point or is not possible at all. In the verification phase,this has the consequence that an individual very well may be rejectedeven though she in fact should be authorized, which results in asignificant degradation of verification performance on average for awhole population. Clearly, it is desirable not to erroneously rejectauthorized individuals, i.e. a low false rejection rate (FRR) isrequired. Thus, individuals having biometric characteristics notsuitable for extraction of a reference point will either experienceenrolment failure or a high FRR in the verification phase.

SUMMARY OF THE INVENTION

It is an object of the present invention to overcome this problem, andto provide a way of generating a template from biometric data associatedwith an individual.

This object is achieved by a method of generating a template frombiometric data associated with an individual, and a method of verifyingidentity of an individual by employing biometric data as defined in theindependent claims. Additional embodiments of the present invention aredefined in the dependent claims, and further objects of the presentinvention will become apparent through the following description.

In a first aspect of the present invention, a method of generating atemplate from biometric data associated with an individual is provided.The method comprises the steps of deriving a positional reference pointof the biometric data and a measure of reliability for the positionalreference point, calculating a contribution indicator for the derivedpositional reference point, and extracting a first feature set from thebiometric data, which first set is extracted taking into account thederived positional reference point, if the derived reference point isconsidered reliable. However, should the reference point be considerednot reliable, the extraction of the first feature set is undertakeninvariably of the derived positional reference point. Further, themethod comprises the step of generating a template from the extractedfirst feature set and associating the template with the contributionindicator.

In a second aspect of the present invention, a method of verifying theidentity of an individual by employing biometric data is provided. Themethod comprises the steps of deriving a positional reference point ofthe biometric data of the individual, if a contribution indicator beingcalculated during enrolment of the individual indicates that thepositional reference point was considered reliable at enrolment.Further, the method comprises the steps of extracting, if thecontribution indicator indicates reliability, a first feature set fromthe biometric data, which first feature set is extracted taking intoaccount the derived positional reference point, and generating atemplate from the extracted first feature set. Finally, the methodcomprises the step of comparing the generated template to at least oneenrolled template to check for correspondence, wherein the identity ofthe individual is verified if correspondence exists.

In a third aspect of the present invention, a device for generating atemplate from biometric data associated with an individual is provided.The device comprises a sensor and a processor, which sensor is arrangedto derive a positional reference point of the biometric data of theindividual and a measure of reliability for the positional referencepoint. The processor is arranged to calculate a contribution indicatorfor the derived positional reference point. The sensor is furtherarranged to extract a first feature set from the biometric data, whichfirst feature set is extracted taking into account the derivedpositional reference point, if the positional reference point can beconsidered reliable. Moreover, the processor is further arranged togenerate a template from the extracted first feature set and associatethe template with the contribution indicator.

In a fourth aspect of the invention, a device for verifying identity ofan individual by employing biometric data is provided. The devicecomprises a sensor and a processor. The sensor is arranged to derive apositional reference point of the biometric data of the individual, if acontribution indicator being calculated during enrolment of theindividual indicates that the positional reference point was consideredreliable at enrolment, and further to extract a first feature set fromthe biometric data, which first feature set is extracted taking intoaccount the derived positional reference point, if the contributionindicator indicates reliability. The processor is arranged to generate atemplate from the extracted first feature set and to compare thegenerated template to at least one enrolled template to check forcorrespondence, wherein the identity of the individual is verified ifcorrespondence exists.

A basic idea of the present invention is to selectively employ one of atleast two different feature extraction processes when generating abiometric template of an individual. An individual offers aphysiological property, such as a fingerprint, an iris, an ear, a face,etc., from which biometric data can be derived, to a sensor of anenrolment authority. In the following, the property to be discussed willbe fingerprints, even though any suitable biometric property may beused. From the fingerprint, a positional reference point of thebiometric data is derived. The derivation of the positional referencepoint may be accomplished using any appropriate method out of a numberof known methods. Such a reference point could be the location of acore, a delta, a weighted average of minutiae coordinates, or alike.Typically, the reference point includes a core of a fingerprintexpressed as a three-dimensional coordinate denoted by means of x_(r),y_(r), and angle α_(r). Further, it is determined whether the derivedpositional reference point can be considered reliable, and acontribution indicator is calculated for the derived positionalreference point. Depending on the reliability of the derived referencepoint, one of the two different feature extraction processes isselected; either the first feature set is extracted using a method whichis invariant of the derived reference point, or a method is used takinginto account the derived reference point. The better the estimation ofthe reference point is, the more reliable the reference point-dependentextraction method is. Finally, the biometric template is generated usingthe extracted first feature set and the generated template is associatedwith the contribution indicator for subsequent verification.

In order to be able to qualify the estimation of the reference point,the contribution indicator is calculated for the derived referencepoint, and if an analysis indicates that the reference point is indeedpresent in the individual's fingerprint and further can be detectedrobustly, the contribution indicator is given a value of, say, 1.However, if no reference point can be found, the contribution indicatoris given a value of 0.

In a first exemplifying scenario, if the contribution indicator has avalue of 1, or being very close to 1, the biometric template to begenerated from the first extracted feature set is generated using theextraction method taking into account the derived reference point, sincethis indicates a good estimation of the reference point and consequentlythat particular method of extraction can be considered reliable in thisparticular scenario. Thus, in this first scenario, presence of referencepoint in the biometric is signaled by the contribution indicator.

In a second exemplifying scenario, assuming that the contributionindicator has a value of 0, or being very close to 0, the biometrictemplate to be generated from the first extracted feature set isgenerated using the reference point-invariant extraction method, sincethis indicates a poor estimation of the reference point and consequentlythe extraction method taking into account the derived reference pointcannot be considered reliable in this particular scenario. Instead, anextraction method being invariant of the derived positional referencepoint is used. Thus, in this second scenario, absence of reference pointin the biometric is signaled by the contribution indicator.

In an embodiment of the present invention, a second feature set isextracted from the biometric data. In this embodiment, the template isgenerated from anyone or both of the extracted feature sets, and thecontribution indicator is further taken into account to determinecontribution of the respective feature set in the generated template.

In this particular embodiment, the reliability indicator can assume anyvalue in the range from 0 to 1. Of course, a different grading ispossible. As in the previously described scenarios, if the value of thecontribution indicator is close to 1, the biometric template to begenerated from the first extracted feature set is generated using theextraction method taking into account the derived reference point,whereas if the value of the contribution indicator is close to 0, thebiometric template to be generated from the first extracted feature setis generated using the reference point-invariant extraction method.Thus, only the first feature set need be extracted in case thecontribution indicator indicates a very good, or very poor, estimationof the reference point.

However, the biometric template could be generated from subsets offeatures derived from the first and second feature sets, in which thecontribution indicator determines the absolute or relative number offeatures used from both sets. Hence, in a third exemplifying scenario,assuming that the contribution indicator has a value of e.g. 0.5, i.e.the reliability of the derived reference point is considered good, butnot outstanding, the first feature set is extracted using the referencepoint-dependent method. Further, the second feature set is extractedusing the reference point-invariant method, and the biometric templateto be generated from anyone or both of the extracted feature sets isgenerated from a combination of the first and second feature set, sincethis indicates a fairly good estimation of the reference point andconsequently an extracted first feature set which can be consideredacceptably reliable, or even highly reliable in parts. Consequently,subsets of features can be taken from the first extracted set andcombined with subsets of features taken from the second extracted set tocreate a biometric template.

In line with the above, in an embodiment of the present invention, thecontribution indicator itself indicates, depending on the gradingselected, the contribution of the respective feature set in thegenerated template. For instance, a value of 0.5 could indicate that a50/50-weight should be given for the two feature sets in the generatedtemplate. In an alternative embodiment, a separate feature set indicatoris used for indicating the contribution of the respective feature set inthe generated template. In such an alternative embodiment, thecontribution indicator is only used for qualifying the estimation of thereference point and not for indicating the weight of the respectivefeature set in the generated template.

As can be seen, the present invention is advantageous, e.g. in that acombination of two different, and supplemental, feature extractionprocesses are used for generating a biometric template of an individualduring enrolment. Thus, previously discussed problems in the prior artrelated to alignment issues are mitigated or overcome by the inventionin that a feature extraction process taking reference point data intoaccount is employed, while a reference point-invariant featureextraction process is employed should the estimation of a positionalreference point from biometric data be poor or not possible at all. Withthe present invention, a reliable feature set is likely to be used forgenerating the biometric template.

The first feature set is derived, in case reference point estimation isgood or at least acceptable, using an extraction process taking intoaccount a biometric data reference point. An exemplifyingreference-point extraction process will be described in the detaileddescription of embodiments of the invention

The second feature set (or the first feature set in case of poorreference point estimation) is derived using a reference point-invariantextraction process, and can be derived using e.g. either (a) summaryfeatures analyzed over a complete fingerprint image or (b) dedicatedtransforms resulting in translation and/or rotation-invariant features.In the detailed description of embodiments of the invention, an approachfalling under category (a) will be described. US patent applicationUS2007/0266427 assigned to the present assignee and incorporated hereinby reference discloses such a method. During verification of anindividual, the individual offers a corresponding physiological propertyfrom which biometric data can be derived, in this particular example afingerprint, to a sensor of a verification authority. A contributionindicator calculated during enrolment of the individual is acquired andconsidered to determine whether to use (a) a feature extraction methodtaking reference point into account or (b) a feature extraction methodbeing independent of reference point. In case the enrolment contributionindicator indicates that a reference point can be reliably derived, apositional reference point of the biometric data is derived, and a firstfeature set is extracted taking into account the derived positionalreference point, since a corresponding extraction method was used duringenrolment. However, should the contribution indicator calculated duringenrolment indicate that estimation of the reference point is poor; areference-point invariant method will be used for extraction. In thatparticular case, there is no need to derive a reference point duringverification. Then, a template is generated from the extracted firstfeature set, which set accordingly has been extracted using a methodcorresponding to the method used during enrolment. When the verificationtemplate has been generated from the first feature set, the verificationtemplate is compared to at least one template generated duringenrolment. If correspondence exists, the identity of the individual canbe verified.

In case a combination of feature sets are used for the individual in theenrolment phase, then the same combination of features is used whenverifying the individual. Thus, in an embodiment of the presentinvention, a positional reference point of the biometric data isderived. A contribution indicator calculated during enrolment of theindividual is acquired for the derived positional reference point, whichcontribution indicator indicates a contribution of the respectivefeature set in the generated template. Then, a first feature set isextracted using the reference point-dependent method; while a secondfeature set is extracted using the reference point-invariant method. Atemplate is generated from a combination of the extracted feature setson the basis of the contribution indicator. Finally, the verificationtemplate is compared to at least one template generated duringenrolment. If correspondence exists, the identity of the individual canbe verified.

In alternative embodiment, the previously discussed feature setindicator is used for indicating the contribution of the respectivefeature set in the generated template. When the verification templatehas been generated from the appropriate feature set(s), the verificationtemplate is compared to at least one template generated duringenrolment. If correspondence exists, the identity of the individual canbe verified.

It should be noted that in practice, the verification template of theindividual will most likely not be identical to the correspondingenrolment due to noise, misalignment, measurement errors, etc. Apredetermined threshold value to be exceeded may be used in the step ofcomparing the verification template to the enrolment template. If thetwo templates are considered to match each other to a certain extent,i.e. a comparison value is above the threshold, the match is consideredgood enough.

Although the use of unprotected biometric templates has been describedin the above, it should be noted that generation and matching ofprotected, i.e. encrypted, templates further may be employed in thepresent invention.

In the case where one single extracted feature set is employed togenerate a template, the measure of reliability could replace thecontribution indicator. Thus, the measure of reliability could indicatewhich one of the two different feature extraction processes is selected;either the method is used which is invariant of the derived referencepoint, or the method is used which takes into account the derivedreference point. The better the estimation of the reference point is,the more reliable the reference point-dependent extraction method is.For instance, for a measure of reliability having a value of 1, or beingvery close to 1, the biometric template to be generated from the firstextracted feature set is generated using the extraction method takinginto account the derived reference point, while for a measure ofreliability having a value of 0, or being very close to 0, the biometrictemplate to be generated from the first extracted feature set isgenerated using the reference point-invariant extraction method.

Note that when verification of an individual's identity is performed inthe present invention, this verification may imply either thatauthentication of an individual is performed or that identification ofan individual is performed. In authentication, the individual claims tohave a certain identity and offered biometric data is compared withstored biometric data (linked to the claimed identity) in order toverify correspondence between the offered and stored data. Inidentification, the offered biometric data is compared with a pluralityof stored available biometric data sets, in order to verifycorrespondence between the offered and stored data. In any case, theoffered data is compared to one or more stored data sets. It is clearthat the term “verification” may denote either “authentication” or“identification” throughout the application, depending on the context inwhich the term is used.

Further features of, and advantages with, the present invention willbecome apparent when studying the appended claims and the followingdescription. Those skilled in the art realize that different features ofthe present invention can be combined to create embodiments other thanthose described in the following. It is noted that the invention relatesto all possible combinations of features recited in the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other aspects of the present invention will now be describedin more detail, with reference to the appended drawings showingembodiments of the invention.

FIG. 1 shows a device for generating a template from biometric dataassociated with an individual in accordance with an embodiment of theinvention.

FIG. 2 shows a device for verifying identity of an individual byemploying biometric data in accordance with an embodiment of theinvention.

FIG. 3 shows a device for generating a template from biometric dataassociated with an individual in accordance with a further embodiment ofthe invention.

FIG. 4 shows a device for verifying identity of an individual byemploying biometric data in accordance with a further embodiment of theinvention.

DETAILED DESCRIPTION

FIG. 1 shows a device 100 for generating a template from biometric dataassociated with an individual in accordance with an embodiment of theinvention. Thus, the device 100 illustrated in FIG. 1 is employed forenrolling in individual in a biometric identification system.

The enrolment device 100 comprises a sensor 101 for extracting featuresets from an offered biometrical property of an individual 102, e.g. afingerprint. From the fingerprint, the sensor 101 derives a positionalreference point of the biometric data and a measure of reliability forthe positional reference point. A processor 103 calculates acontribution indicator for the derived positional reference point atblock 104, and it is determined whether the derived positional referencepoint can be considered reliable. As illustrated in FIG. 1, theprocessor 103 feeds this information back to the sensor 101, anddepending on the reliability of the derived reference point, one of twodifferent feature extraction processes is selected; either a firstfeature set is extracted by the sensor 101 using a method which isinvariant of the derived reference point, or the sensor 101 uses amethod taking into account the derived reference point. The better theestimation of the reference point is, the more reliable the referencepoint-dependent extraction method is. Finally, the biometric template isgenerated at block 105 using the extracted first feature set. Thegenerated biometric template is stored at memory 106 (located inside, orexternal to, the processor) together with the contribution indicator tosubsequently indicate to a verifier which extraction method is to beused for the particular generated biometric template. For securityreasons, the generated template may be encrypted at block 105 beforebeing transferred to memory 106. Further, the memory 106 is notnecessarily an integrated part of the enrolment device 100, but may belocated remotely from the device, even in a different part of the world.

FIG. 2 shows a device 200 for verifying identity of an individual byemploying biometric data in accordance with an embodiment of theinvention. The verification device 200 of FIG. 2 very much resembles theenrolment device 100 of FIG. 1 structurally.

During verification of the individual 202, the individual offers acorresponding physiological property from which biometric data can bederived, in this particular example a fingerprint, to a sensor 201 ofthe verification device 200. A contribution indicator calculated duringenrolment of the individual is acquired by block 204 of processor 203from memory 206 and provided to the sensor 201. The contributionindicator is considered by the sensor 201 to determine whether to use(a) a feature extraction method taking reference point into account or(b) a feature extraction method being independent of reference point. Incase the enrolment contribution indicator indicates that a referencepoint can be reliably derived, a positional reference point of thebiometric data is derived by the sensor 201, and a first feature set isextracted taking into account the derived positional reference point,since a corresponding extraction method was used during enrolment.However, should the contribution indicator calculated during enrolmentindicate that estimation of the reference point is poor; areference-point invariant method will be used for extraction. In thatparticular case, there is no need to derive a reference point duringverification. Then, block 205 generates a template from the extractedfirst feature set, which set accordingly has been extracted using amethod corresponding to the method used during enrolment. When theverification template has been generated from the first feature set, theverification template is compared at block 207 to at least one templategenerated during enrolment and fetched from the memory 206. Ifcorrespondence exists, the identity of the individual 202 can beverified.

In a practical situation, the enrolment authority may coincide with theverifier, but they may also be distributed. As an example, if thebiometric system is used for banking applications, all larger offices ofthe bank will typically be allowed to enroll new individuals into thesystem, such that a distributed enrolment authority is created. If,after enrolment, the individual wishes to withdraw money from such anoffice while using her biometric data as authentication, this officewill assume the role of verifier. On the other hand, if the user makes apayment in a convenience store using her biometric data asauthentication, the store will assume the role of the verifier, but itis unlikely that the store ever will act as enrolment authority.

As can be seen hereinabove, the individual has access to a device thatcontains a biometric sensor and has computing capabilities. In practice,the device could comprise a fingerprint sensor integrated in a smartcard or a camera for iris or facial recognition in a mobile phone or aPDA. It is assumed that the individual has obtained the device from atrusted authority (e.g. a bank, a national authority, a government) andthat she therefore trusts this device.

Now, a prior art method for extracting feature sets from biometric data,which method takes into account a derived positional reference point, isdescribed in “Practical Biometric Authentication with TemplateProtection” by Pim Tuyls et al, AVBPA 2005, LNCS 3546, pp. 436-446,2005, Springer-Verlag Berlin Heidelberg 2005. This method could be usedin the present invention for extracting the first feature set.

With reference to section 2.3 denoted “Fingerprint Feature Extraction”,a fixed length feature vector representation is presented, of which theelements can be compared one by one directly. The selected featurevector describes the global shape of the fingerprint by means of thelocal orientations of the ridge lines. In order to allow for directcomparison of the feature vectors, without requiring a registrationstage during matching, the feature vectors have to be pre-aligned duringfeature extraction. For this purpose, the core point (i.e. the uppermostpoint of the innermost curving ridge) is used. These core points areautomatically extracted using a known likelihood ratio-based algorithm.To describe the shape of the fingerprint, two types of feature vectorsare extracted from gray scale fingerprint images.

The first feature vector is a squared directional field. It is evaluatede.g. at a regular grid of 16 by 16 points with spacings of e.g. 8pixels, which is centered at the core point. At each of the 256positions, the squared directional field is coded in a vector of twoelements, representing the x- and y-values, resulting in a512-dimensional feature vector.

The second feature vector is the Gabor response of the fingerprint.After subtraction of the spatial local mean, the fingerprint image isfiltered by a set of four complex Gabor filters, which are given by:

h _(Gabor)(x, y)=exp(−(x ² +y ²)/2σ²)*exp(j 2πf·(x sin θ+y cos θ))

The orientations θ are set to 0, π/4, π/2, and 3π/4, the spatialfrequency f is tuned to the average spatial ridge-valley frequency(f=0.11), and the width of the filter a is set such that the entireorientation range is covered (σ=3.5). The absolute values of the outputimages are taken, which are subsequently filtered by a low-pass Gaussianwindow. Again, samples are taken at a regular grid of 16 by 16 pointswith spacings of 8 pixels and centered at the core point. The resultingfeature vector is of length 1024. The resulting feature vector that isused for matching is a concatenation of the squared directional fieldand the Gabor response. It describes the global shape of the fingerprintin 1536 elements. Thus, the extracted feature set is determined based ona derived positional reference point (“the core point”).

Regarding the reliability of a derived core point, reference is made tosection 3.1 denoted “Enrolment”. The input feature vectors of person iis denoted as X_(i)={X_(i,j)d}_(j=1 . . . M). A binary string Q(X_(i,j))is constructed from the feature vector X_(i,j). The t-th component ofQ(X_(i,j)) for a fixed user i=1, . . . , N is called reliable, if thevalues (Q(X_(i,j)))_(t) for j=1 . . . M are all equal. The booleanvector Bi ∈{0, 1}^(k) denotes the reliable bits. Its t-th entry equalsone if the t-th component of Q(X_(i,j)) is reliable otherwise the t-thentry is zero.

Further, a prior art method for extracting feature sets from biometricdata, which method operates independently of a positional referencepoint, is described in US patent application US2007/0266427 assigned tothe present assignee. In brief, the method describes derivation of afirst feature set X comprising n+1 components from a first set ofbiometric data X_(T) and is transformed into a feature density functionf_(X,s)(x),

${{f_{X,s}(x)} = {{s(x)}*{\sum\limits_{i = 0}^{n}{\delta ( {x - x_{i}} )}}}},$

by performing a summation of the different components, and convolvingthe resulting sum with an averaging function, whereby a new firstfeature vector X_(F)=f_(X,s)(x) is created that advantageously can beused in a helper data scheme. This will typically be a sampled versionof the density function, which results in feature vectors of equal andfinite dimensions regardless of the number n+1 of components present inthe feature set X.

Returning to the present invention, reference is made to FIG. 3, where afurther embodiment is described. In this particular embodiment, atemplate is generated using a combination of extracted feature sets. Thedevice 300 of FIG. 3 is structurally very similar to the device 100 ofFIG. 1. However, the contribution indicator is in this embodimentfurther provided to template generating block 305. The sensor 301 of theenrolment device 300 derives a positional reference point of thebiometric data offered by the individual 302, and it is furtherdetermined whether the derived positional reference point can beconsidered reliable. The processor 303 calculates a contributionindicator for the derived positional reference point at block 304. Inthis particular embodiment, the reliability of the derived referencepoint is considered good, but not outstanding, and it is thus decidedthat the biometric template should be generated from subsets of featuresderived from a first and a second feature set. The processor 303 feedsthis information back to the sensor 301, which extracts a first featureset using a method taking into account the derived reference point, anda second feature set using a method which is invariant of the derivedreference point. Block 304 further provides template generation block305 with the contribution indicator.

Finally, the biometric template is generated at block 305 using subsetsof features derived from the first and second feature set, wherein thecontribution indicator determines the absolute or relative number offeatures used from both sets in the template. The generated biometrictemplate is stored at memory 306 together with the contributionindicator to subsequently indicate to a verifier the contribution of therespect feature set in the generated template.

FIG. 4 shows a device 400 for verifying identity of an individual byemploying biometric data in accordance with a further embodiment of theinvention. The verification device 400 of FIG. 4 very much resembles theenrolment device 300 of FIG. 3 structurally.

The individual 402 offers her fingerprint, to the sensor 401 of theverification device 400. A contribution indicator calculated duringenrolment of the individual is acquired by block 404 of processor 403from memory 406 and provided to the sensor 401. In this particularembodiment, the reliability of the derived reference point is consideredgood, but not outstanding, and it is thus decided that the biometrictemplate should be generated from subsets of features derived from afirst and a second feature set. The sensor 401 of the verificationdevice 400 derives a positional reference point of the biometric dataoffered by the individual 402, and further extracts a first feature setusing a method taking into account the derived reference point, and asecond feature set using a method which is invariant of the derivedreference point. Then, the biometric template is generated at block 405using subsets of features derived from the first and second feature set,wherein the contribution indicator supplied by memory 406 determines theabsolute or relative number of features used from both sets in thetemplate. Finally, when the verification template has been generatedfrom the first and second feature set, the verification template iscompared at block 407 to at least one template generated duringenrolment and fetched from the memory 406. If correspondence exists, theidentity of the individual 402 can be verified.

It is clear that the devices of the present invention are arranged withmicroprocessors or other similar electronic equipment having computingcapabilities, for example programmable logic devices such as ASICs,FPGAs, CPLDs etc. Further, the microprocessors execute appropriatesoftware stored in memories, on discs or on other suitable media foraccomplishing tasks of the present invention.

Further, it is obvious to a skilled person that the data communicatedin, and in connection to, the devices described above can further beprotected using standard cryptographic techniques such as SHA-1, MD5,AES, DES or RSA. Before any data is exchanged between devices (duringenrolment as well as during verification) comprised in the system, adevice might want some proof on the authenticity of another other devicewith which communication is established. For example, it is possiblethat the enrolment authority must be ensured that a trusted device didgenerate the enrolment data received. This can be achieved by usingpublic key certificates or, depending on the actual setting, symmetrickey techniques. Moreover, it is possible that the enrolment authoritymust be ensured that the user device can be trusted and that it has notbeen tampered with. Therefore, the devices may contain mechanisms thatallow the enrolment authority to detect tampering. For example, PhysicalUncloneable Functions (PUFs) may be used. A PUF is a function that isrealized by a physical system, such that the function is easy toevaluate but the physical system is hard to characterize. Depending onthe actual setting, communications between devices might have to besecret and authentic. Standard cryptographic techniques that can be usedare Secure Authenticated Channels (SACs) based on public key techniquesor similar symmetric techniques.

Also note that the enrolment data and the verification data may becryptographically concealed by means of employing a one-way hashfunction, or any other appropriate cryptographic function that concealsthe enrolment data and verification in a manner such that it iscomputationally infeasible to create a plain text copy of theenrolment/verification data from the cryptographically concealed copy ofthe enrolment/verification data. It is, for example possible to use akeyed one-way hash function, a trapdoor hash function, an asymmetricencryption function or even a symmetric encryption function.

1. A method of generating a template from biometric data associated withan individual, the method comprising the steps of: deriving a positionalreference point of said biometric data of the individual and a measureof reliability for the positional reference point; calculating acontribution indicator for the derived positional reference point;extracting a first feature set from said biometric data, said firstfeature set being extracted taking into account the derived positionalreference point, if the measure of reliability indicates that thepositional reference point can be considered reliable; and generating atemplate from the extracted first feature set and associating thetemplate with the calculated contribution indicator.
 2. The method ofclaim 1, further comprising the step of: extracting a second feature setfrom said biometric data; wherein the step of generating a templatefurther comprises the step of: generating a template from anyone or bothof the extracted feature sets, wherein said contribution indicator istaken into account to determine contribution of the respective featureset in the generated template.
 3. The method of claim 2, wherein thetemplate is generated from subsets of features derived from the firstand second feature sets, and the contribution indicator determinesabsolute or relative number of features used from the respective set inthe generated template.
 4. The method of claims 2, further comprisingthe step of: storing the generated template and the contributionindicator, said contribution indicator indicating the contribution ofthe respective feature set in the generated template.
 5. The method ofclaims 2, further comprising the step of: storing the generated templateand a feature set indicator, said feature set indicator indicating thecontribution of the respective feature set in the generated template. 6.The method of claim 1, wherein said first feature set is extractedinvariably of the derived positional reference point, if thecontribution indicator indicates that the positional reference point isconsidered not reliable.
 7. The method of claims 2, wherein said secondfeature set is extracted invariably of the derived positional referencepoint.
 8. A method of verifying identity of an individual by employingbiometric data, the method comprising the steps of: deriving apositional reference point of said biometric data of the individual, ifa contribution indicator being calculated during enrolment of theindividual indicates that the positional reference point was consideredreliable at enrolment; extracting a first feature set from saidbiometric data, said first feature set being extracted taking intoaccount the derived positional reference point, if the contributionindicator indicates reliability; generating a template from theextracted first feature set; and comparing the generated template to atleast one enrolled template to check for correspondence, wherein theidentity of the individual is verified if correspondence exists.
 9. Themethod of claim 8, further comprising the step of: extracting a secondfeature set from said biometric data; wherein the step of generating atemplate further comprises the step of: generating a template fromanyone or both of the extracted feature sets, wherein said contributionindicator is taken into account to determine contribution of therespective feature set in the generated template.
 10. The method ofclaim 8, wherein said first feature set is extracted invariably of thepositional reference point, if the contribution indicator beingcalculated during enrolment indicates that the positional referencepoint is considered not reliable.
 11. A device (100) for generating atemplate from biometric data associated with an individual (102), thedevice comprising: a sensor (101); and a processor (103); wherein thesensor is arranged to derive a positional reference point of saidbiometric data of the individual and a measure of reliability for thepositional reference point; and the processor is arranged to calculate acontribution indicator for the derived positional reference; said sensorfurther being arranged to extract a first feature set from saidbiometric data, said first feature set being extracted taking intoaccount the derived positional reference point, if the measure ofreliability indicates that the positional reference point can beconsidered reliable; and said processor further being arranged togenerate a template from the extracted first feature set and associatethe template with the calculated contribution indicator.
 12. The device(300) of claim 11, wherein said sensor (301) further is arranged toextract a second feature set from said biometric data; and saidprocessor (303) further is arranged to generate a template from anyoneor both of the extracted feature sets, wherein said contributionindicator is taken into account to determine contribution of therespective feature set in the generated template.
 13. A device (200) forverifying identity of an individual by employing biometric data, thedevice comprising: a sensor (201); and a processor (203); wherein thesensor is arranged to derive a positional reference point of saidbiometric data of the individual (202), if a contribution indicatorbeing calculated during enrolment of the individual indicates that thepositional reference point was considered reliable at enrolment; and toextract a first feature set from said biometric data, said first featureset being extracted taking into account the derived positional referencepoint, if the contribution indicator indicates reliability; saidprocessor being arranged to generate a template from the extracted firstfeature set; and to compare the generated template to at least oneenrolled template to check for correspondence, wherein the identity ofthe individual is verified if correspondence exists.
 14. The device ofclaim 13, wherein said sensor (401) further is arranged to extract asecond feature set from said biometric data; and said processor (403)further is arranged to generate a template from anyone or both of theextracted feature sets, wherein said contribution indicator is takeninto account to determine contribution of the respective feature set inthe generated template.
 15. A computer program product comprisingcomputer-executable components for causing a device to perform the stepsrecited in claim 1 when the computer-executable components are run on aprocessing unit included in the device.